{"id":"W2797303068","doi":"10.1051/epjconf/201817601011","title":"First application of the optimal estimation method to retrieve temperature from pure rotational raman scatter lidar measurements","year":2018,"lang":"en","type":"article","venue":"EPJ Web of Conferences","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Lidar; Remote sensing; Backscatter (email); Calibration; Raman spectroscopy; Environmental science; Atmospheric temperature; Temperature measurement; Computational physics; Materials science; Computer science; Physics; Meteorology; Optics; Geology; Telecommunications; Thermodynamics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007130506,0.000548652,0.0005076071,0.0004965611,0.0003003622,0.0004931307,0.0004324954,0.0006364833,0.0006586707],"category_scores_gemma":[0.001847618,0.0003024978,0.0005789293,0.0003769064,0.0003086863,0.000492332,0.0005655624,0.0004873696,0.0002932263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002244305,"about_ca_system_score_gemma":0.0006026131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003295491,"about_ca_topic_score_gemma":0.003149691,"domain_scores_codex":[0.9996333,0.00009115696,0.0000217748,0.00008817526,0.0001318376,0.00003369191],"domain_scores_gemma":[0.9996455,0.0001473654,0.00002687665,0.00006400707,0.0001062239,0.00001002134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003103651,0.000178446,0.008801508,0.0003683243,0.0002465478,0.0002780211,0.000187532,0.2469629,0.1947491,0.007432799,0.00248844,0.537996],"study_design_scores_gemma":[0.00001981359,0.00009143825,0.003046404,0.0000113666,0.00003569947,0.0001574225,0.00002916636,0.9276233,0.06369078,0.001700447,0.003548485,0.00004567656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06172644,0.0004612493,0.9351068,0.0001062902,0.00008177375,0.000050808,0.0001452425,0.0007376812,0.001583754],"genre_scores_gemma":[0.3864258,0.0002605797,0.6116027,0.00005791487,0.00005617811,0.00005913277,0.0001827696,0.0001160005,0.001238922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003295491,"threshold_uncertainty_score":0.006552577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159314007049653,"score_gpt":0.2615248790616525,"score_spread":0.2455934783566872,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}